clockfly / lanarky

Open-source framework to deploy LLM applications in production. Built on top of FastAPI

Lanarky

Lanarky is an open-source framework to deploy LLM applications in production. It is built on top of FastAPI and comes with batteries included.

Table of Contents

• 🚀 Features
• ❓ Why?
• 💾 Installation
• 🔥 Build your first LLM app
• 📍 Roadmap
• 🤩 Stargazers
• 🤝 Contributing
• 📝 License
• ✨ Want to build LLM applications with us?

🚀 Features

• 🌐 multi-mode token streaming
• 💬 simple gradio chatbot UI for fast prototyping
• 🔗 supports LangChain applications
• 🗄️ multiple LLM caching strategies

See Roadmap for upcoming features.

❓ Why?

There are great low-code/no-code solutions in the open source to deploy your LLM projects. However, most of them are opinionated in terms of cloud or deployment code. This project aims to provide users with a cloud-agnostic and deployment-agnostic solution which can be easily integrated into existing backend infrastructures.

💾 Installation

The library is available on PyPI and can be installed via pip.

pip install lanarky

You can find the full documentation at https://lanarky.readthedocs.io/en/latest/.

🔥 Build your first Langchain app

from dotenv import load_dotenv
from fastapi import FastAPI
from langchain import ConversationChain
from langchain.chat_models import ChatOpenAI

from lanarky import LangchainRouter

load_dotenv()
app = FastAPI()

langchain_router = LangchainRouter(
    langchain_url="/chat",
    langchain_object=ConversationChain(
        llm=ChatOpenAI(temperature=0), verbose=True
    ),
    streaming_mode=0
  )
app.include_router(langchain_router)

See examples/ for list of available demo examples.

Create a .env file using .env.sample and add your OpenAI API key to it before running the examples.

📍 Roadmap

• Add support for LangChain
• Add Gradio UI for fast prototyping
• Add support for in-memory, Redis and GPTCache LLM caching
• Add support for LlamaIndex
• Add support for Guidance
• Add SQL database integration
• Add support for Rebuff

🤩 Stargazers

Leave a ⭐ if you find this project useful.

🤝 Contributing

Contributions are more than welcome! If you have an idea for a new feature or want to help improve lanarky, please create an issue or submit a pull request on GitHub.

See CONTRIBUTING.md for more information.

Contributors

⚖️ License

The library is released under the MIT License.

✨ Want to build LLM applications with us?

Are you interested in building LLM applications with us? We would love to hear from you! Reach out to us on Twitter @lanarky_io.

Let's connect and explore the possibilities of working together to create amazing LLM applications with Lanarky!